While most subsequent works in feed-forward style transfer focused on the 'universal' setting, where the style can be arbitrary, there have been several papers which consider more sophisticated objective functions for training style transfer models specialized to a particular style. Sanakoyeu et al. [1]} departed from the framework of [2]} in many ways. First, it replaced the gram matrix loss of Gatys with an adversarial loss based on a learned discriminator[3]}. Second, rather than relying on a pretrained representaion, this work learned representations amenable to a particular type of stylization from scratch. Third, it attempted to learn a style-invariant content representation by leveraging a cycle loss (recovering the input photo from the stylized result). Kotovenko et al. [4]} refines these ideas, using a contrastive loss on representations extracted from the stylized output and original content to learn style-invariant representations of content, and a contrastive loss on the stylized output and the original style to learn a content-invariant representations of style. While these models must be retrained for new styles, they produce beautiful results within the chosen style.
